The Impact of Public Data Openness on Urban Green Total Factor Productivity: A Quasi-Natural Experiment Based on the Establishment of Government Data Open Platforms

The study treats the establishment of government data open platforms as a quasi-natural experiment and constructs a theoretical analysis framework of "public data openness—technological innovation, industrial structure upgrading and public supervision—green total factor productivity". Based on 2011-2022 panel data of 282 prefecture-level and above cities in China, the study adopts the entropy method, the SBM-GML model, a staggered difference-in-differences (DID) model and a mediating effect model to measure urban green total factor productivity (GTFP), identify the causal effect of public data openness, and test its transmission mechanisms. The results show that the establishment of a government data open platform significantly raises urban GTFP by 0.038, equivalent to about 3.2% of the sample mean, and the conclusion remains robust to the parallel trend test, placebo test, PSM-DID, alternative variable measurement, exclusion of concurrent policies and heterogeneity-robust estimators. Mechanism analysis indicates that green technology innovation, industrial structure upgrading and public environmental attention are three significant channels. The effect is stronger in eastern and large cities than in central-western and small and medium-sized cities. This study provides empirical support and policy reference for releasing the value of public data and promoting the high-quality green transformation of China's cities, also offer reference for other developing emerging economies.